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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spatially resolved structural order in low-temperature liquid electrolyte

Determining the degree and the spatial extent of structural order in liquids is a grand challenge. Here, we are able to resolve the structural order in a model organic electrolyte of 1 M lithium hexafluorophosphate (LiP 6 ) dissolved in 1:1 (v/v) ethylene carbonate:diethylcarbonate by developing an integrated method of liquid-phase transmission electron microscopy (TEM), cryo-TEM operated at -30°C, four-dimensional scanning TEM, and data analysis based on deep learning. This study reveals the presence of short-range order (SRO) in the high–salt concentration domains of the liquid electrolyte from liquid phase separation at the low temperature. Molecular dynamics simulations suggest the SRO originates from the Li + - (PF 6 - ) n (n > 2) local structural order induced by high LiP 6 salt concentration.

42 ENGINEERING↗
DOE OSTI2024

Evaluating Physics-Informed Neural Network Performance for Seismic Discrimination between Earthquakes and Explosions

In this article, we evaluate adding a weak physics constraint, that is, a physics‐based empirical relationship, to the loss function with a physics‐informed manner in local distance explosion discrimination in the hope of improving the generalization capability of the machine learning (ML) model. We compare the proposed model with the two‐branch model we previously developed, as well as with a pure data‐driven model. Unexpectedly, the proposed model did not consistently outperform the pure data‐driven model. By varying the level of inconsistency in the training data, we find this approach is modulated by the strength of the physics relationship. In conclusion, this result has important implications for how to best incorporate physical constraints in ML models.

58 GEOSCIENCES↗
DOE OSTI2018

Neutron Residual Stress Mapping for Spent Nuclear Fuel Storage Canister Weldment

Many radioactive materials within the nuclear fuel cycle present a significant hazard. Such materials include spent nuclear fuel, high-level waste, legacy waste and other nuclear materials that have no current outlet than storage. These materials are often held in long-term storage as an interim stage within their lifecycle. Lifecycles can include reuse or disposal. Safety and security of spent nuclear fuel (SNF) interim storage installations are very important, due to a great concentration of fission products, actinides and activation products. Stress corrosion cracking of interim storage containers has been identified as a high priority data gap by the Department of Energy. However, little has been done with regards to the canister weld material properties and their impact on stress corrosion, until recently from Sandia National Laboratory canister mockup program. Furthermore, because no post-weld heat treatment was required, the associated high tensile residual stress within these canister welds can drive the initiation and growth of stress corrosion cracking (SCC) cracks. In this work, we carried out in-situ neutron residual stress mapping on a weld plate sectioned from Sandia mock-up canister wall. Significant residual stress profiles were identified from the received longitudinal weld sample. The details of the weldment residual stress program development at ORNL are presented in this progress report. The generated residual stress profile from neutron diffraction technique is similar to that of Sandia residual stress data obtained from the deep hole drilling approach.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Cyber-Attack Detection and Accommodation for the Energy Delivery System

The goals of this project were to create a software system with a suite of key algorithms for cyber-attack detection and accommodation providing domain layer protection for critical power generation assets. Example assets included gas and steam turbines, heat recovery steam generators, and electrical generators. The aggressive algorithm goals were aimed at reducing the false positive rates in threat detection to <1% using learnings from many evolving disciplines (power turbine and generator physics, power system modeling, modern control theory, system identification, machine learning, deep learning, mathematics and data science). Additional goals for the algorithms involved localizing threats on-the-fly to know in which monitoring node the effects of attacks are present, and then providing accommodation to keep the system running uninterrupted much of the time in the presence of the attack. Accommodation had a performance goal of providing resiliency when up to 50% of monitoring nodes are in an attack state.

cybersecurity, cyber-physical↗
DOE OSTI2022

Deep Learning At Depth: Estimating subsurface parameters from geophysical monitoring data

Geophysical imaging techniques are a non-invasive way to image the subsurface and understand both subsurface solid (rock/soil) and fluid property distributions and their evolution in time. Inversions of the geophysical data, such as Electrical Resistance Tomography (ERT) data, are solved to estimate the subsurface property distributions, such as conductivity, and many inversion techniques smooth out sharp gradients in rock or fluid property distributions. Sharp gradients in subsurface properties tend to be present in situations with complex subsurface structures, which are common in many subsurface applications. We have successfully demonstrated that it is possible to inform, or constrain, inversions with neural networks trained on synthetic data with complex subsurface structures. Initial results suggest this process may be optimizable to yield property distributions that better represent the true property distributions than the same inversion process without the neural network constraint. Future work would optimize the neural network performance for this application and then apply the synthetic-data trained neural network to real data to understand the utility and performance of this technique for real data sets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Flavour Decomposition with Precision Pion and Kaon Cross Sections

Key properties of the nucleon are encoded in the correlation between their longitudinal momentum and their transverse position, such as the orbital angular momentum. This correlation is expressed through Generalized Parton Distributions (GPDs), which can be understood as spatial densities at different values of the longitudinal momentum of the quark. Validation of our understanding of hard exclusive reactions ($\pi^+$, $K^+$, $\pi^0$, $\gamma$) is an essential aspect towards such 3D hadron imaging and potential future flavor decomposition. The key to extracting GPDs from experiment is QCD factorization theorems. To validate the meson factorization theorems and potentially extract flavor separated GPDs from experiment, one has measure the separated longitudinal and transverse cross sections and their $t$ and $Q^2$ dependencies. Recent data and prospects for deep exclusive pion, both charged and neutral, and kaon electroproduction are presented, which conceptually would allow for flavor separations.

Horn, Tanja↗
DOE OSTI2024

Cybersecurity for the Operational Technology Environment (CyOTE) (Final Technical Report)

Electric grids have historically been susceptible to both physical attacks and environmental hazards but the implementation of smart grids, remote management, and self-healing networks, has now made the grid vulnerable to cyber attacks. To address risks introduced by routable connectivity, utilities must establish dynamic solutions to identify, protect, detect, respond to, and recover from cyber security threats and vulnerabilities. In response to the evolving threat landscape U.S. Department of Energy-Office of Cybersecurity, Energy Security, and Emergency Response (DOE CESER) initiated the Cybersecurity for the OT Environment (CyOTE) pilot program, a U.S. Department of Energy (DOE) effort designed to leverage U.S. intelligence capabilities to prevent, detect, or mitigate a cyber attack on utility operational technology (OT) networks. As part of the CyOTE pilot, The Southern Company (Southern Company or Southern) researched, evaluated and deployed emerging Commercial off the Shelf (COTS) technologies and cyber security monitoring architectures to provide previously unrealized network visibility and situational awareness through deep packet inspection and data analytics. This Final Scientific/Technical Report documents the objectives, methodology, lessons learned, and results of Southern Company’s participation in the CyOTE pilot from December 2018 to September 2023.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

CLASH-VLT: DISSECTING THE FRONTIER FIELDS GALAXY CLUSTER MACS J0416.1-2403 WITH ∼800 SPECTRA OF MEMBER GALAXIES

We present VIMOS-Very Large Telescope (VLT) spectroscopy of the Frontier Fields cluster MACS J0416.1-2403 ( z = 0.397). Taken as part of the CLASH-VLT survey, the large spectroscopic campaign provided more than 4000 reliable redshifts over ∼600 arcmin{sup 2}, including ∼800 cluster member galaxies. The unprecedented sample of cluster members at this redshift allows us to perform a highly detailed dynamical and structural analysis of the cluster out to ∼2.2 r {sub 200} (∼4 Mpc). Our analysis of substructures reveals a complex system composed of a main massive cluster ( M {sub 200} ∼ 0.9 × 10{sup 15} M {sub ⊙} and σ{sub V,r200} ∼ 1000 km s{sup −1}) presenting two major features: (i) a bimodal velocity distribution, showing two central peaks separated by Δ V {sub rf} ∼ 1100 km s{sup −1} with comparable galaxy content and velocity dispersion, and (ii) a projected elongation of the main substructures along the NE–SW direction, with a prominent sub-clump ∼600 kpc SW of the center and an isolated BCG approximately halfway between the center and the SW clump. We also detect a low-mass structure at z ∼ 0.390, ∼10′ south of the cluster center, projected at ∼3 Mpc, with a relative line-of-sight velocity of Δ V{sub rf} ∼ −1700 km s{sup −1}. The cluster mass profile that we obtain through our dynamical analysis deviates significantly from the “universal” NFW, being best fit by a Softened Isothermal Sphere model instead. The mass profile measured from the galaxy dynamics is found to be in relatively good agreement with those obtained from strong and weak lensing, as well as with that from the X-rays, despite the clearly unrelaxed nature of the cluster. Our results reveal an overall complex dynamical state of this massive cluster and support the hypothesis that the two main subclusters are being observed in a pre-collisional phase, in agreement with recent findings from radio and deep X-ray data. In this article, we also release the entire redshift catalog of 4386 sources in the field of this cluster, which includes 60 identified Chandra X-ray sources and 105 JVLA radio sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Falling Outer Rotation Curves of Star-forming Galaxies at 0.6 ≲ z ≲ 2.6 Probed with KMOS{sup 3D} and SINS/zC-SINF

We exploit the deep, resolved, Hα kinematic data from the KMOS{sup 3D} and SINS/zC-SINF surveys to examine the largely unexplored outer-disk kinematics of star-forming galaxies (SFGs), out to the peak of cosmic star formation. Our sample contains 101 SFGs, representative of the more massive (9.3≲logM{sub ∗}/M{sub ⊙}≲11.5) main sequence population at 0.6 ≤ z ≤ 2.6. Through a novel stacking approach, we are able to constrain a representative rotation curve extending out to ∼4 effective radii. This average rotation curve exhibits a significant drop in rotation velocity beyond the turnover, with a slope of ΔV/ΔR=−0.26{sub −0.09}{sup +0.10} in units of normalized coordinates V/V {sub max} and R/R {sub turn}. This result confirms that the fall-off seen in some individual galaxies is a common feature of our sample of high-z disks. The outer fall-off strikingly deviates from the flat or mildly rising rotation curves of local spiral galaxies that have similar masses. Through a comparison with models that include baryons and dark matter, we demonstrate that the falling stacked rotation curve is consistent with a high mass fraction of baryons, relative to the total dark matter halo (m {sub d} ≳ 0.05), in combination with a sizeable level of pressure support in the outer disk. These findings agree with recent studies demonstrating that high-z star-forming disks are strongly baryon-dominated within the disk scale, and furthermore suggest that pressure gradients caused by large, turbulent gas motions are present even in their outer disks. These results are largely independent of our model assumptions, such as the presence of stellar bulges, the effect of adiabatic contraction, and variations in halo concentration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

The morphology and structure of galaxies reflect their star formation and assembly histories. We use the framework of mutual information (MI) to quantify the interdependence among several structural variables and to rank them according to their relevance for predicting the specific star formation rate (SSFR) by comparing the MI of the predictor variables with the SSFR and penalizing variables that are redundant. We apply this framework to study ~3700 face-on star-forming galaxies (SFGs) with varying degrees of bulge dominance and central concentration and with stellar mass $M$ * ≈ 10 9 $M$ ⊙ at redshift $z$ = 0.02–0.12. We use the Sloan Digital Sky Survey (SDSS) Stripe 82 deep $i$-band imaging data, which improve measurements of asymmetry and bulge dominance indicators. We find that star-forming galaxies are a multiparameter family. In addition to $M$ * , asymmetry emerges as the most powerful predictor of SSFR residuals of SFGs, followed by bulge prominence/concentration. Star-forming galaxies with higher asymmetry and stronger bulges have higher SSFR at a given $M$ * . In conclusion, the asymmetry reflects both irregular spiral arms and lopsidedness in seemingly isolated SFGs and structural perturbations by galaxy interactions or mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1970

Pioneer 7 TDS requirements

Near-earth and deep space tracking and data acquisition requirements for Pioneer 7 space probe

Source record↗
NASA NTRS1976

Viking Mars encounter

Various phases of planetary operations related to the Viking mission to Mars are described. Topics discussed include: approach phase, Mars orbit insertion, prelanding orbital activities, separation, descent and landing, surface operations, surface sampling and operations starting, orbiter science and radio science, Viking 2, Deep Space Network and data handling.

Source record↗